Neural network-based predictive control system for energy optimization in sports facilities: a case study

Elnour, Mariam, Mohammedsherif, Hamdi, Fadli, Fodil, Meskin, Nader, M. Ahmad, Ahmad, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400, Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247 and Hodorog, Andrei ORCID: https://orcid.org/0000-0002-4701-5643 2021. Neural network-based predictive control system for energy optimization in sports facilities: a case study. Presented at: 38th CIB W78 conference on Information and Communication Technologies, Luxembourg, 11-15 October 2021. Proc. of the Joint Conference CIB W78 - LDAC 2021. Luxembourg: CIB, pp. 734-743.

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Abstract

Given the increased global energy demand and its associated environmental impacts, the management and optimization of sports facilities is becoming imperative as they are characterized by high energy demand and occupancy profiles. In this work, the theory of model predictive control ȋMPCȌ is combined with neural networks for temperature setpoint selection to achieve energy and performance optimization of sports facilities. It is demonstrated using the building information model ȋBIMȌ of a sports hall in the sports complex of Qatar University. MPC systems are powerful as they allow integrated dynamic optimization that accounts for the future system behavior in the decision-making process, while neural networks are advantageous for their ability to represent complex interdependencies with high accuracy. The proposed approach was able to achieve a total energy savings of around ͵͵Ψ. Considerations about the network performance, MPC settings tuning, and optimization sub-optimality or failure are essential during the design and implementation phases of the proposed system.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Publisher:	CIB
Date of First Compliant Deposit:	17 November 2021
Date of Acceptance:	15 August 2021
Last Modified:	29 Nov 2022 10:04
URI:	https://orca.cardiff.ac.uk/id/eprint/145402

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